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arXiv Machine Learning
Oct 21

Curriculum Learning for Generative Flexible Docking

The paper introduces a curriculum learning strategy for generative flexible docking, a task that predicts protein‑ligand complex configurations from an apo protein structure and a ligand SMILES string. By gradually incorporating examples with large conformational changes during training, the authors enhance an existing generative model, achieving a 46.52% top‑1 success rate on the PDBBind ESMFold apo benchmark and outperforming prior baselines. The approach leverages diffusion and flow‑based generative models to model the posterior distribution over ligand poses and protein conformations conditioned on the apo input.

By Wenyin Zhou, Hossein Azizpour
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arXiv Machine Learning
6d ago

Matcha: Multi-Stage Riemannian Flow Matching for Accurate and Physically Valid Molecular Docking

Matcha is a new molecular docking method that refines ligand poses through three flow-matching stages—translation, rotation, and torsion—before applying GNINA for minimization, filtering, and ranking. It achieves high physical plausibility and is approximately 28 times faster than current large-scale co‑folding models, outperforming existing benchmarks. The authors provide code, benchmarking artifacts, and model checkpoints on GitHub and Zenodo.

By Daria Frolova, Talgat Daulbaev, Egor Sevriugov, Sergei A. Nikolenko, Dmitry N. Ivankov, Ivan Oseledets, Marina A. Pak
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arXiv Machine Learning
6d ago

MLIPDiff: Guiding Denoising Diffusion Models for Structure-Based Drug Design via Machine-Learned Interatomic Potentials

MLIPDiff is an inference‑time guidance technique that enhances pretrained denoising diffusion models for ligand generation by incorporating physics‑informed guidance from a machine‑learned interatomic potential (UMA). It combines four weighted energy terms—protein‑ligand interaction, ligand strain, steric clash, and pocket anchoring—to eliminate protein‑ligand collisions while preserving local geometry, without requiring model retraining. On the CrossDocked2020 benchmark, MLIPDiff produces clash‑free, physically plausible ligand poses and achieves superior binding‑affinity metrics compared to both the base diffusion model and NucleusDiff.

By Yiyan Liao, Amin Tavakoli, Yu Zhang, Anima Anandkumar
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arXiv Machine Learning
Jun 5

An accurate nucleic acid-small molecule docking framework via geometric deep learning with large-scale pretraining

arXiv:2606. 05198v1 Announce Type: cross Abstract: Nucleic acids are increasingly recognized as therapeutic targets beyond conventional protein-centered drug discovery, yet accurate and efficient docking of small molecules to nucleic acid structures remains challenging.

By Shi Li (College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, P. R. China), Xujun Zhang (College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, P. R. China), Mingquan Liu (Faculty of Health Sciences, University of Macau, Macau SAR, China), Hui Zhang (College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, P. R. China, Shanghai Innovation Institute, Shanghai, China), Shuoying Jia (College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, P. R. China, Shanghai Innovation Institute, Shanghai, China), Yu Kang (College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, P. R. China, Shanghai Innovation Institute, Shanghai, China), Tingjun Hou (College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, P. R. China, Zhejiang Provincial Key Laboratory for Intelligent Drug Discovery and Development, Jinhua Institute of Zhejiang University, Zhejiang, China), Peichen Pan (College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, P. R. China, Zhejiang Provincial Key Laboratory for Intelligent Drug Discovery and Development, Jinhua Institute of Zhejiang University, Zhejiang, China)
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